We utilize distribution plots
to study the variation in embedding statisticsDistribution plots show 0, 5, 50, 95, and 100-percentiles for embedding statistics at various TSP sizes, thus visualizing how the statistics changes with problem scale, and are easily implemented via TensorBoard [1]}.
of three identical models:
(1) GNN-Max, which represents our best model configuration from Section : autoregressive decoding, Graph ConvNet encoder with Max aggregation and BatchNorm with batch statistics;
(2) GNN-Sum, which uses Sum aggregation for the Graph ConvNet and shows comparatively poor generalization beyond training size, see Figure REF ;
and (3) GNN-Max + learnt BN, which uses standard BatchNorm, i.e. learns statistics from the training data, and also shows comparatively poor generalization, see Figure REF .
